In [ ]:
import numpy as np
import h5py
import pandas as pd
from sklearn.model_selection import train_test_split

import matplotlib.pyplot as plt
import seaborn as sns

Load files¶

In [ ]:
# Load CSV files into pandas DataFrames
data_files = {
    "kyle": {
        "jumping": {
            "hand": "data/jumping/kjh_data.csv",
            "front": "data/jumping/kjf_data.csv",
            "back": "data/jumping/kjb_data.csv",
        },
        "walking": {
            "hand": "data/walking/kwh_data.csv",
            "front": "data/walking/kwf_data.csv",
            "back": "data/walking/kwb_data.csv",
        },
    },
    "abdellah": {
        "jumping": {
            "backright": "data/jumping/ajbrp_data.csv",
            "frontleft": "data/jumping/ajflp_data.csv",
        },
        "walking": {
            "backright": "data/walking/awbrp_data.csv",
            "frontleft": "data/walking/awflp_data.csv",
        },
    },
    "liam": {
        "jumping": {
            "backleft": "data/jumping/ljlbp_data.csv",
            "backright": "data/jumping/ljbrp_data.csv",
        },
        "walking": {
            "lefthand": "data/walking/lwlh_data.csv",
            "leftpocket": "data/walking/lwlp_data.csv",
            "righthand": "data/walking/lwrh_data.csv",
            "rightpocket": "data/walking/lwrp_data.csv",
        },
    },
}

# Save DataFrames to HDF5 file
with h5py.File("activityData.hdf5", "w") as hdf5_file:
    for person, activities in data_files.items():
        for activity, areas in activities.items():
            for area, file_path in areas.items():
                df = pd.read_csv(file_path)
                hdf5_file.create_dataset(f"{person}/{activity}/{area}", data=df.to_numpy())
In [ ]:
In [ ]:
features = ["Time (s)", "Linear Acceleration x (m/s^2)", "Linear Acceleration y (m/s^2)", "Linear Acceleration z (m/s^2)", "Absolute acceleration (m/s^2)"]

def preprocess_data(hdf5_file):
    data_frames = {
        "walking": [],
        "jumping": []
    }

    for person in hdf5_file.keys():
        for activity in hdf5_file[person].keys():
            activity_data = []
            
            for area in hdf5_file[person][activity].keys():
                dataset = hdf5_file[person][activity][area]
                df = pd.DataFrame(dataset, columns=features)

                # Remove first 5 seconds and last 5 seconds of data
                start_time = df["Time (s)"].min()
                end_time = df["Time (s)"].max()
                df = df[(df["Time (s)"] > start_time + 5) & (df["Time (s)"] < end_time - 5)]

                activity_data.append(df)

            # Concatenate all DataFrames related to the same activity for all persons
            data_frames[activity].append(pd.concat(activity_data, ignore_index=True))

    # Concatenate all DataFrames for each activity
    for activity in data_frames.keys():
        data_frames[activity] = pd.concat(data_frames[activity], ignore_index=True)

    return data_frames
In [ ]:
features = ["Time (s)", "Linear Acceleration x (m/s^2)", "Linear Acceleration y (m/s^2)", "Linear Acceleration z (m/s^2)", "Absolute acceleration (m/s^2)"]

def preprocessed_data(hdf5_file):
    data_frames = {}

    for person in hdf5_file.keys():
        data_frames[person] = {}
        
        for activity in hdf5_file[person].keys():
            activity_data = []
            
            for area in hdf5_file[person][activity].keys():
                dataset = hdf5_file[person][activity][area]
                df = pd.DataFrame(dataset, columns=features)

                # Remove first 5 seconds and last 5 seconds of data
                start_time = df["Time (s)"].min()
                end_time = df["Time (s)"].max()
                df = df[(df["Time (s)"] > start_time + 5) & (df["Time (s)"] < end_time - 5)]

                activity_data.append(df)

            # Concatenate all DataFrames related to the same activity for the same person
            data_frames[person][activity] = pd.concat(activity_data, ignore_index=True)

    return data_frames

with h5py.File("activityData.hdf5", "r") as hdf5_file:
    preprocessed_data1 = preprocessed_data(hdf5_file)
In [ ]:
def plot_acceleration_data1(preprocessed_data, person, activity):
    df = preprocessed_data[person][activity]
    
    fig, axes = plt.subplots(3, 1, figsize=(12, 12), sharex=True)
    
    axes[0].plot(df["Time (s)"], df["Linear Acceleration x (m/s^2)"], label="x-axis", color='red')
    axes[0].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (X-axis)")
    axes[0].set_ylabel("Acceleration (m/s^2)")
    axes[0].legend()

    axes[1].plot(df["Time (s)"], df["Linear Acceleration y (m/s^2)"], label="y-axis", color='blue')
    axes[1].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (Y-axis)")
    axes[1].set_ylabel("Acceleration (m/s^2)")
    axes[1].legend()

    axes[2].plot(df["Time (s)"], df["Linear Acceleration z (m/s^2)"], label="z-axis", color='green')
    axes[2].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (Z-axis)")
    axes[2].set_xlabel("Time (s)")
    axes[2].set_ylabel("Acceleration (m/s^2)")
    axes[2].legend()

    plt.tight_layout()
    plt.show()
In [ ]:
plot_acceleration_data1(preprocessed_data1, "kyle", "walking")
plot_acceleration_data1(preprocessed_data1, "kyle", "jumping")
plot_acceleration_data1(preprocessed_data1, "abdellah", "walking")
plot_acceleration_data1(preprocessed_data1, "abdellah", "jumping")
plot_acceleration_data1(preprocessed_data1, "liam", "walking")
plot_acceleration_data1(preprocessed_data1, "liam", "jumping")
In [ ]:
# Read the HDF5 file and preprocess the data
with h5py.File("activityData.hdf5", "r") as hdf5_file:
    preprocessed_data = preprocess_data(hdf5_file)

# Access the walking and jumping DataFrames
walking_data = preprocessed_data["walking"]
jumping_data = preprocessed_data["jumping"]

jumping_data
Out[ ]:
Time (s) Linear Acceleration x (m/s^2) Linear Acceleration y (m/s^2) Linear Acceleration z (m/s^2) Absolute acceleration (m/s^2)
0 5.013552 -2.933793 11.037301 3.719913 12.011116
1 5.023593 -2.416790 11.771116 3.588315 12.540975
2 5.033633 -2.126614 12.072641 2.344312 12.480663
3 5.043674 -1.933966 11.783559 0.807484 11.968480
4 5.053715 -1.944011 11.346681 0.003355 11.512009
... ... ... ... ... ...
47677 97.299237 2.557272 -13.176605 5.568800 14.531830
47678 97.309172 2.165217 -12.374189 4.263774 13.266065
47679 97.319107 1.679658 -11.476837 3.132702 12.014693
47680 97.329042 1.012671 -10.766406 2.379240 11.072569
47681 97.338976 0.386872 -10.057555 2.027284 10.267130

47682 rows × 5 columns

In [ ]:
import seaborn as sns
import matplotlib.pyplot as plt

def visualize_data(df, title):
    # Create a pair plot to visualize relationships between features
    sns.pairplot(df)
    plt.suptitle(f'{title} Pair Plot', y=1.02, fontsize=14)
    plt.show()

    # Create a correlation heatmap to visualize correlations between features
    corr = df.corr()
    plt.figure(figsize=(10, 8))
    sns.heatmap(corr, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
    plt.title(f'{title} Correlation Heatmap', fontsize=14)
    plt.show()

# Visualize walking data
visualize_data(walking_data, "Walking")

# Visualize jumping data
visualize_data(jumping_data, "Jumping")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_12172\3055048744.py in <module>
     21 visualize_data(jumping_data, "Jumping")
     22 
---> 23 print(walking_data.corr.to_string())

AttributeError: 'function' object has no attribute 'to_string'
In [ ]:
walking_data.corr()
Out[ ]:
Time (s) Linear Acceleration x (m/s^2) Linear Acceleration y (m/s^2) Linear Acceleration z (m/s^2) Absolute acceleration (m/s^2)
Time (s) 1.000000 -0.073506 -0.453393 -0.122208 0.306061
Linear Acceleration x (m/s^2) -0.073506 1.000000 -0.009823 0.247693 0.262401
Linear Acceleration y (m/s^2) -0.453393 -0.009823 1.000000 0.209656 -0.423656
Linear Acceleration z (m/s^2) -0.122208 0.247693 0.209656 1.000000 -0.029331
Absolute acceleration (m/s^2) 0.306061 0.262401 -0.423656 -0.029331 1.000000
In [ ]:
import matplotlib.ticker as ticker
def plot_acceleration_data2(df, person, activity):
    fig, axes = plt.subplots(3, 1, figsize=(12, 12), sharex=True)
    
    axes[0].plot(df["Time (s)"], df["Linear Acceleration x (m/s^2)"], label="x-axis", color='red')
    axes[0].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (X-axis)")
    axes[0].set_ylabel("Acceleration (m/s^2)")
    axes[0].xaxis.set_major_locator(ticker.AutoLocator())
    axes[0].legend()

    axes[1].plot(df["Time (s)"], df["Linear Acceleration y (m/s^2)"], label="y-axis", color='blue')
    axes[1].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (Y-axis)")
    axes[1].xaxis.set_major_locator(ticker.AutoLocator())
    axes[1].set_ylabel("Acceleration (m/s^2)")
    axes[1].legend()

    axes[2].plot(df["Time (s)"], df["Linear Acceleration z (m/s^2)"], label="z-axis", color='green')
    axes[2].set_title(f"{activity.capitalize()} Activity for {person.capitalize()} (Z-axis)")
    axes[2].set_xlabel("Time (s)")
    axes[2].set_ylabel("Acceleration (m/s^2)")
    axes[2].xaxis.set_major_locator(ticker.AutoLocator())
    axes[2].legend()

    plt.tight_layout()
    plt.show()
In [ ]:
import pandas as pd

# Replace the file path with the actual path to your CSV file
for name in ['ajbrp','ajflp', 'kjb','kjf','ljbrp','ljlbp']:
    csv_file_path = "data/jumping/"+str(name)+"_data.csv"
    csv_data = pd.read_csv(csv_file_path)
    if name in ['ajbrp','ajflp']:
        test = 'Abdellah'
    elif name in ['kjb','kjf']:
        test = 'Kyle'
    else:
        test= 'Liam'
    # Assuming the person's name is "Ljlbp" and the activity is "jumping"
    plot_acceleration_data2(csv_data, name, "jumping")